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In this research, two different classifier categories, correlation-based and neural network-based, are investigated for a Farsi isolated word recognizer commanding robotic system. Correlation-based category is divided to time and frequency domains. Moreover, in each of them, three decision making methods, Max, Average, and 10-Max are proposed. In addition, in neural network-based category, LPC is considered to extract the features. At first, separated samples of 4 Farsi pronounced commands (Left, Right, Forward, and Backward) go through a pre-processing section. Three methods of correlation-based category are used independently with the same data base and their performances are evaluated word by word as well as in total case. Finally the results of above mentioned methods are compared. On the other hand, LPC features get extracted independently from output of preprocessing section, and are used as inputs of the N.N. In this way one result associated to N.N.-based method is produced. Simulation results show that frequency-domain correlation-based method introduces the best recognition but, it is close to the LPC-based N.N. system. Finally it is preferred to use LPC due to lower processing time with 87.5% recognition.